Unimolecular Dual Incretins Maximize Metabolic Benefits in Rodents, Monkeys, and Humans
Bibliographic record
Abstract
We report the discovery and translational therapeutic efficacy of a peptide with potent, balanced co-agonism at both of the receptors for the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). This unimolecular dual incretin is derived from an intermixed sequence of GLP-1 and GIP, and demonstrated enhanced antihyperglycemic and insulinotropic efficacy relative to selective GLP-1 agonists. Notably, this superior efficacy translated across rodent models of obesity and diabetes, including db/db mice and ZDF rats, to primates (cynomolgus monkeys and humans). Furthermore, this co-agonist exhibited synergism in reducing fat mass in obese rodents, whereas a selective GIP agonist demonstrated negligible weight-lowering efficacy. The unimolecular dual incretins corrected two causal mechanisms of diabesity, adiposity-induced insulin resistance and pancreatic insulin deficiency, more effectively than did selective mono-agonists. The duration of action of the unimolecular dual incretins was refined through site-specific lipidation or PEGylation to support less frequent administration. These peptides provide comparable pharmacology to the native peptides and enhanced efficacy relative to similarly modified selective GLP-1 agonists. The pharmacokinetic enhancement lessened peak drug exposure and, in combination with less dependence on GLP-1-mediated pharmacology, avoided the adverse gastrointestinal effects that typify selective GLP-1-based agonists. This discovery and validation of a balanced and high-potency dual incretin agonist enables a more physiological approach to management of diseases associated with impaired glucose tolerance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".